Overview

Dataset statistics

Number of variables27
Number of observations50
Missing cells342
Missing cells (%)25.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 KiB
Average record size in memory218.6 B

Variable types

Numeric15
Categorical10
Unsupported2

Alerts

Pos. is highly correlated with Seasons and 15 other fieldsHigh correlation
Seasons is highly correlated with Pos. and 15 other fieldsHigh correlation
Pld is highly correlated with Pos. and 15 other fieldsHigh correlation
W is highly correlated with Pos. and 15 other fieldsHigh correlation
D is highly correlated with Pos. and 13 other fieldsHigh correlation
L is highly correlated with Pos. and 11 other fieldsHigh correlation
GF is highly correlated with Pos. and 15 other fieldsHigh correlation
GA is highly correlated with Pos. and 12 other fieldsHigh correlation
1st is highly correlated with Pos. and 13 other fieldsHigh correlation
2nd is highly correlated with Pos. and 13 other fieldsHigh correlation
3rd is highly correlated with Pos. and 17 other fieldsHigh correlation
4th is highly correlated with 3rd and 5 other fieldsHigh correlation
5th is highly correlated with 1st and 1 other fieldsHigh correlation
6th is highly correlated with D and 3 other fieldsHigh correlation
7th is highly correlated with 1st and 2 other fieldsHigh correlation
T4 is highly correlated with Pos. and 13 other fieldsHigh correlation
T7 is highly correlated with Pos. and 15 other fieldsHigh correlation
Relegated is highly correlated with 1st and 3 other fieldsHigh correlation
Best is highly correlated with Pos. and 15 other fieldsHigh correlation
WP is highly correlated with Pos. and 16 other fieldsHigh correlation
predWP is highly correlated with Pos. and 16 other fieldsHigh correlation
factor is highly correlated with Pos. and 13 other fieldsHigh correlation
Pos. is highly correlated with Seasons and 16 other fieldsHigh correlation
Seasons is highly correlated with Pos. and 14 other fieldsHigh correlation
Pld is highly correlated with Pos. and 14 other fieldsHigh correlation
W is highly correlated with Pos. and 15 other fieldsHigh correlation
D is highly correlated with Pos. and 12 other fieldsHigh correlation
L is highly correlated with Pos. and 11 other fieldsHigh correlation
GF is highly correlated with Pos. and 16 other fieldsHigh correlation
GA is highly correlated with Pos. and 11 other fieldsHigh correlation
1st is highly correlated with Pos. and 11 other fieldsHigh correlation
2nd is highly correlated with Pos. and 13 other fieldsHigh correlation
3rd is highly correlated with Pos. and 15 other fieldsHigh correlation
4th is highly correlated with Pos. and 8 other fieldsHigh correlation
5th is highly correlated with 1stHigh correlation
6th is highly correlated with 3rdHigh correlation
7th is highly correlated with 1st and 1 other fieldsHigh correlation
T4 is highly correlated with Pos. and 12 other fieldsHigh correlation
T7 is highly correlated with Pos. and 15 other fieldsHigh correlation
Relegated is highly correlated with 2nd and 1 other fieldsHigh correlation
Best is highly correlated with Pos. and 13 other fieldsHigh correlation
WP is highly correlated with Pos. and 14 other fieldsHigh correlation
predWP is highly correlated with Pos. and 16 other fieldsHigh correlation
factor is highly correlated with Pos. and 10 other fieldsHigh correlation
Pos. is highly correlated with Seasons and 14 other fieldsHigh correlation
Seasons is highly correlated with Pos. and 12 other fieldsHigh correlation
Pld is highly correlated with Pos. and 12 other fieldsHigh correlation
W is highly correlated with Pos. and 14 other fieldsHigh correlation
D is highly correlated with Pos. and 11 other fieldsHigh correlation
L is highly correlated with Pos. and 8 other fieldsHigh correlation
GF is highly correlated with Pos. and 13 other fieldsHigh correlation
GA is highly correlated with Pos. and 10 other fieldsHigh correlation
1st is highly correlated with Pos. and 7 other fieldsHigh correlation
2nd is highly correlated with Pos. and 9 other fieldsHigh correlation
3rd is highly correlated with Pos. and 13 other fieldsHigh correlation
4th is highly correlated with 3rd and 1 other fieldsHigh correlation
5th is highly correlated with 1stHigh correlation
7th is highly correlated with 1stHigh correlation
T4 is highly correlated with Pos. and 11 other fieldsHigh correlation
T7 is highly correlated with Pos. and 13 other fieldsHigh correlation
Relegated is highly correlated with 2nd and 1 other fieldsHigh correlation
Best is highly correlated with Pos. and 13 other fieldsHigh correlation
WP is highly correlated with Pos. and 13 other fieldsHigh correlation
predWP is highly correlated with Pos. and 14 other fieldsHigh correlation
factor is highly correlated with 3rd and 2 other fieldsHigh correlation
1st is highly correlated with 6th and 4 other fieldsHigh correlation
3rd is highly correlated with Relegated and 2 other fieldsHigh correlation
Relegated is highly correlated with 3rd and 1 other fieldsHigh correlation
6th is highly correlated with 1st and 2 other fieldsHigh correlation
Debut is highly correlated with ClubHigh correlation
Since/Last App. is highly correlated with 1st and 4 other fieldsHigh correlation
Club is highly correlated with 1st and 8 other fieldsHigh correlation
4th is highly correlated with Since/Last App. and 3 other fieldsHigh correlation
7th is highly correlated with 1st and 2 other fieldsHigh correlation
5th is highly correlated with 1st and 2 other fieldsHigh correlation
Pos. is highly correlated with Club and 14 other fieldsHigh correlation
Club is highly correlated with Pos. and 23 other fieldsHigh correlation
Seasons is highly correlated with Pos. and 12 other fieldsHigh correlation
Pld is highly correlated with Pos. and 12 other fieldsHigh correlation
W is highly correlated with Pos. and 14 other fieldsHigh correlation
D is highly correlated with Pos. and 13 other fieldsHigh correlation
L is highly correlated with Pos. and 16 other fieldsHigh correlation
GF is highly correlated with Pos. and 13 other fieldsHigh correlation
GA is highly correlated with Pos. and 13 other fieldsHigh correlation
1st is highly correlated with Club and 8 other fieldsHigh correlation
2nd is highly correlated with Club and 13 other fieldsHigh correlation
3rd is highly correlated with Club and 15 other fieldsHigh correlation
4th is highly correlated with Pos. and 12 other fieldsHigh correlation
5th is highly correlated with Club and 10 other fieldsHigh correlation
6th is highly correlated with Club and 4 other fieldsHigh correlation
7th is highly correlated with Club and 6 other fieldsHigh correlation
T4 is highly correlated with Club and 9 other fieldsHigh correlation
T7 is highly correlated with Pos. and 13 other fieldsHigh correlation
Debut is highly correlated with Pos. and 5 other fieldsHigh correlation
Since/Last App. is highly correlated with Club and 17 other fieldsHigh correlation
Relegated is highly correlated with Club and 7 other fieldsHigh correlation
Best is highly correlated with Pos. and 9 other fieldsHigh correlation
WP is highly correlated with Pos. and 18 other fieldsHigh correlation
predWP is highly correlated with Pos. and 15 other fieldsHigh correlation
factor is highly correlated with Pos. and 6 other fieldsHigh correlation
1st has 43 (86.0%) missing values Missing
2nd has 41 (82.0%) missing values Missing
3rd has 40 (80.0%) missing values Missing
4th has 39 (78.0%) missing values Missing
5th has 36 (72.0%) missing values Missing
6th has 37 (74.0%) missing values Missing
7th has 33 (66.0%) missing values Missing
T4 has 36 (72.0%) missing values Missing
T7 has 23 (46.0%) missing values Missing
Debut has 1 (2.0%) missing values Missing
Since/Last App. has 1 (2.0%) missing values Missing
Relegated has 8 (16.0%) missing values Missing
Best has 1 (2.0%) missing values Missing
WP has 1 (2.0%) missing values Missing
predWP has 1 (2.0%) missing values Missing
factor has 1 (2.0%) missing values Missing
Pos. is uniformly distributed Uniform
Club is uniformly distributed Uniform
Pos. has unique values Unique
Club has unique values Unique
GF has unique values Unique
GA has unique values Unique
GD is an unsupported type, check if it needs cleaning or further analysis Unsupported
Pts is an unsupported type, check if it needs cleaning or further analysis Unsupported
Seasons has 1 (2.0%) zeros Zeros
Pld has 1 (2.0%) zeros Zeros
W has 1 (2.0%) zeros Zeros
D has 1 (2.0%) zeros Zeros
L has 1 (2.0%) zeros Zeros
GF has 1 (2.0%) zeros Zeros
GA has 1 (2.0%) zeros Zeros

Reproduction

Analysis started2022-05-09 21:59:02.687169
Analysis finished2022-05-09 21:59:36.693887
Duration34.01 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Pos.
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.5
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:36.821859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.45
Q113.25
median25.5
Q337.75
95-th percentile47.55
Maximum50
Range49
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation14.57737974
Coefficient of variation (CV)0.5716619505
Kurtosis-1.2
Mean25.5
Median Absolute Deviation (MAD)12.5
Skewness0
Sum1275
Variance212.5
MonotonicityStrictly increasing
2022-05-09T16:59:36.989245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
2.0%
381
 
2.0%
281
 
2.0%
291
 
2.0%
301
 
2.0%
311
 
2.0%
321
 
2.0%
331
 
2.0%
341
 
2.0%
351
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
11
2.0%
21
2.0%
31
2.0%
41
2.0%
51
2.0%
61
2.0%
71
2.0%
81
2.0%
91
2.0%
101
2.0%
ValueCountFrequency (%)
501
2.0%
491
2.0%
481
2.0%
471
2.0%
461
2.0%
451
2.0%
441
2.0%
431
2.0%
421
2.0%
411
2.0%

Club
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Manchester United
 
1
Bournemouth
 
1
Swansea City
 
1
Queens Park Rangers
 
1
Birmingham City
 
1
Other values (45)
45 

Length

Max length23
Median length17
Mean length12.88
Min length6

Characters and Unicode

Total characters644
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowManchester United
2nd rowArsenal
3rd rowChelsea
4th rowLiverpool
5th rowTottenham Hotspur

Common Values

ValueCountFrequency (%)
Manchester United1
 
2.0%
Bournemouth1
 
2.0%
Swansea City1
 
2.0%
Queens Park Rangers1
 
2.0%
Birmingham City1
 
2.0%
Wolverhampton Wanderers1
 
2.0%
Portsmouth1
 
2.0%
Burnley1
 
2.0%
Derby County1
 
2.0%
Watford1
 
2.0%
Other values (40)40
80.0%

Length

2022-05-09T16:59:37.150819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city10
 
11.4%
united5
 
5.7%
athletic3
 
3.4%
town3
 
3.4%
manchester2
 
2.3%
west2
 
2.3%
albion2
 
2.3%
wanderers2
 
2.3%
sheffield2
 
2.3%
tottenham1
 
1.1%
Other values (56)56
63.6%

Most occurring characters

ValueCountFrequency (%)
e59
 
9.2%
t50
 
7.8%
n43
 
6.7%
o40
 
6.2%
i39
 
6.1%
38
 
5.9%
r38
 
5.9%
a34
 
5.3%
l31
 
4.8%
d28
 
4.3%
Other values (36)244
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter516
80.1%
Uppercase Letter87
 
13.5%
Space Separator38
 
5.9%
Other Punctuation1
 
0.2%
Open Punctuation1
 
0.2%
Close Punctuation1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e59
11.4%
t50
 
9.7%
n43
 
8.3%
o40
 
7.8%
i39
 
7.6%
r38
 
7.4%
a34
 
6.6%
l31
 
6.0%
d28
 
5.4%
s25
 
4.8%
Other values (12)129
25.0%
Uppercase Letter
ValueCountFrequency (%)
C16
18.4%
B11
12.6%
W9
10.3%
S7
8.0%
A7
8.0%
H5
 
5.7%
U5
 
5.7%
T4
 
4.6%
L3
 
3.4%
N3
 
3.4%
Other values (10)17
19.5%
Space Separator
ValueCountFrequency (%)
38
100.0%
Other Punctuation
ValueCountFrequency (%)
&1
100.0%
Open Punctuation
ValueCountFrequency (%)
[1
100.0%
Close Punctuation
ValueCountFrequency (%)
]1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin603
93.6%
Common41
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e59
 
9.8%
t50
 
8.3%
n43
 
7.1%
o40
 
6.6%
i39
 
6.5%
r38
 
6.3%
a34
 
5.6%
l31
 
5.1%
d28
 
4.6%
s25
 
4.1%
Other values (32)216
35.8%
Common
ValueCountFrequency (%)
38
92.7%
&1
 
2.4%
[1
 
2.4%
]1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e59
 
9.2%
t50
 
7.8%
n43
 
6.7%
o40
 
6.2%
i39
 
6.1%
38
 
5.9%
r38
 
5.9%
a34
 
5.3%
l31
 
4.8%
d28
 
4.3%
Other values (36)244
37.9%

Seasons
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.72
Minimum0
Maximum29
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:37.298594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median8
Q315.75
95-th percentile29
Maximum29
Range29
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation9.258289171
Coefficient of variation (CV)0.7899564139
Kurtosis-0.6686494116
Mean11.72
Median Absolute Deviation (MAD)5
Skewness0.7979924642
Sum586
Variance85.71591837
MonotonicityNot monotonic
2022-05-09T16:59:37.441565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
78
16.0%
296
12.0%
55
10.0%
84
 
8.0%
24
 
8.0%
13
 
6.0%
133
 
6.0%
153
 
6.0%
262
 
4.0%
92
 
4.0%
Other values (10)10
20.0%
ValueCountFrequency (%)
01
 
2.0%
13
 
6.0%
24
8.0%
31
 
2.0%
41
 
2.0%
55
10.0%
78
16.0%
84
8.0%
92
 
4.0%
101
 
2.0%
ValueCountFrequency (%)
296
12.0%
262
 
4.0%
251
 
2.0%
241
 
2.0%
221
 
2.0%
181
 
2.0%
161
 
2.0%
153
6.0%
133
6.0%
121
 
2.0%

Pld
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct29
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450.64
Minimum0
Maximum1114
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:37.589566image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.8
Q1198
median310
Q3599.5
95-th percentile1114
Maximum1114
Range1114
Interquartile range (IQR)401.5

Descriptive statistics

Standard deviation355.1616913
Coefficient of variation (CV)0.7881273106
Kurtosis-0.6632750507
Mean450.64
Median Absolute Deviation (MAD)196
Skewness0.7998676537
Sum22532
Variance126139.8269
MonotonicityNot monotonic
2022-05-09T16:59:37.735371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2667
 
14.0%
11146
 
12.0%
763
 
6.0%
4942
 
4.0%
382
 
4.0%
1902
 
4.0%
1982
 
4.0%
3042
 
4.0%
3162
 
4.0%
3542
 
4.0%
Other values (19)20
40.0%
ValueCountFrequency (%)
01
 
2.0%
382
4.0%
421
 
2.0%
763
6.0%
841
 
2.0%
1141
 
2.0%
1521
 
2.0%
1902
4.0%
1982
4.0%
2021
 
2.0%
ValueCountFrequency (%)
11146
12.0%
10001
 
2.0%
9961
 
2.0%
9581
 
2.0%
9241
 
2.0%
8481
 
2.0%
6961
 
2.0%
6081
 
2.0%
5742
 
4.0%
5701
 
2.0%

W
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.88
Minimum0
Maximum687
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:37.891191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q156.25
median93.5
Q3204
95-th percentile589.8
Maximum687
Range687
Interquartile range (IQR)147.75

Descriptive statistics

Standard deviation178.384318
Coefficient of variation (CV)1.068937668
Kurtosis1.366879455
Mean166.88
Median Absolute Deviation (MAD)60.5
Skewness1.51477529
Sum8344
Variance31820.9649
MonotonicityNot monotonic
2022-05-09T16:59:38.052449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
5972
 
4.0%
102
 
4.0%
992
 
4.0%
571
 
2.0%
811
 
2.0%
731
 
2.0%
751
 
2.0%
791
 
2.0%
761
 
2.0%
681
 
2.0%
Other values (37)37
74.0%
ValueCountFrequency (%)
01
2.0%
51
2.0%
102
4.0%
121
2.0%
141
2.0%
171
2.0%
221
2.0%
321
2.0%
361
2.0%
411
2.0%
ValueCountFrequency (%)
6871
2.0%
5972
4.0%
5811
2.0%
4801
2.0%
4441
2.0%
4071
2.0%
3691
2.0%
3411
2.0%
3191
2.0%
2651
2.0%

D
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.88
Minimum0
Maximum314
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:38.215660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.8
Q150.75
median85.5
Q3166.5
95-th percentile278.75
Maximum314
Range314
Interquartile range (IQR)115.75

Descriptive statistics

Standard deviation90.48748701
Coefficient of variation (CV)0.7741913673
Kurtosis-0.6625853051
Mean116.88
Median Absolute Deviation (MAD)58
Skewness0.7347129623
Sum5844
Variance8187.985306
MonotonicityNot monotonic
2022-05-09T16:59:38.381182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
822
 
4.0%
652
 
4.0%
232
 
4.0%
502
 
4.0%
2471
 
2.0%
591
 
2.0%
761
 
2.0%
661
 
2.0%
721
 
2.0%
621
 
2.0%
Other values (36)36
72.0%
ValueCountFrequency (%)
01
2.0%
51
2.0%
91
2.0%
131
2.0%
151
2.0%
171
2.0%
201
2.0%
232
4.0%
431
2.0%
481
2.0%
ValueCountFrequency (%)
3141
2.0%
2901
2.0%
2811
2.0%
2761
2.0%
2741
2.0%
2731
2.0%
2541
2.0%
2471
2.0%
2451
2.0%
2281
2.0%

L
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.88
Minimum0
Maximum394
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:38.544182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.45
Q192.75
median135
Q3239.5
95-th percentile371.2
Maximum394
Range394
Interquartile range (IQR)146.75

Descriptive statistics

Standard deviation107.2476897
Coefficient of variation (CV)0.6426635287
Kurtosis-0.4770620212
Mean166.88
Median Absolute Deviation (MAD)81.5
Skewness0.5964386299
Sum8344
Variance11502.06694
MonotonicityNot monotonic
2022-05-09T16:59:38.712182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1432
 
4.0%
1282
 
4.0%
1801
 
2.0%
1291
 
2.0%
1321
 
2.0%
1111
 
2.0%
1191
 
2.0%
1221
 
2.0%
1381
 
2.0%
791
 
2.0%
Other values (38)38
76.0%
ValueCountFrequency (%)
01
2.0%
191
2.0%
221
2.0%
231
2.0%
391
2.0%
421
2.0%
461
2.0%
471
2.0%
591
2.0%
661
2.0%
ValueCountFrequency (%)
3941
2.0%
3931
2.0%
3731
2.0%
3691
2.0%
3581
2.0%
3551
2.0%
2961
2.0%
2761
2.0%
2591
2.0%
2541
2.0%

GF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE
ZEROS

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean598.34
Minimum0
Maximum2128
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:38.886248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48.35
Q1221.5
median363
Q3744.25
95-th percentile1913.5
Maximum2128
Range2128
Interquartile range (IQR)522.75

Descriptive statistics

Standard deviation584.1719977
Coefficient of variation (CV)0.9763211513
Kurtosis0.6025602136
Mean598.34
Median Absolute Deviation (MAD)238
Skewness1.301206327
Sum29917
Variance341256.9229
MonotonicityNot monotonic
2022-05-09T16:59:39.047203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21281
 
2.0%
2411
 
2.0%
3061
 
2.0%
3391
 
2.0%
2731
 
2.0%
2901
 
2.0%
2921
 
2.0%
2661
 
2.0%
2711
 
2.0%
2761
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
01
2.0%
371
2.0%
471
2.0%
501
2.0%
551
2.0%
661
2.0%
681
2.0%
1051
2.0%
1361
2.0%
1481
2.0%
ValueCountFrequency (%)
21281
2.0%
19561
2.0%
19271
2.0%
18971
2.0%
16761
2.0%
15591
2.0%
14481
2.0%
13331
2.0%
12131
2.0%
11751
2.0%

GA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE
ZEROS

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean598.34
Minimum0
Maximum1415
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:39.307175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90.1
Q1314.75
median462.5
Q3885.75
95-th percentile1367.65
Maximum1415
Range1415
Interquartile range (IQR)571

Descriptive statistics

Standard deviation404.4204526
Coefficient of variation (CV)0.6759040889
Kurtosis-0.7417826262
Mean598.34
Median Absolute Deviation (MAD)296
Skewness0.5947204854
Sum29917
Variance163555.9024
MonotonicityNot monotonic
2022-05-09T16:59:39.471205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10091
 
2.0%
3301
 
2.0%
3831
 
2.0%
4311
 
2.0%
3601
 
2.0%
4191
 
2.0%
3801
 
2.0%
4021
 
2.0%
4201
 
2.0%
4411
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
01
2.0%
781
2.0%
821
2.0%
1001
2.0%
1341
2.0%
1381
2.0%
1421
2.0%
1431
2.0%
1861
2.0%
2141
2.0%
ValueCountFrequency (%)
14151
2.0%
13981
2.0%
13781
2.0%
13551
2.0%
12991
2.0%
12151
2.0%
11211
2.0%
11001
2.0%
10921
2.0%
10421
2.0%

GD
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size528.0 B

Pts
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size528.0 B

1st
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)57.1%
Missing43
Missing (%)86.0%
Memory size528.0 B
1.0
5.0
13.0
3.0

Length

Max length4
Median length3
Mean length3.142857143
Min length3

Characters and Unicode

Total characters22
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)28.6%

Sample

1st row13.0
2nd row3.0
3rd row5.0
4th row1.0
5th row5.0

Common Values

ValueCountFrequency (%)
1.03
 
6.0%
5.02
 
4.0%
13.01
 
2.0%
3.01
 
2.0%
(Missing)43
86.0%

Length

2022-05-09T16:59:39.630801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T16:59:39.789203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.03
42.9%
5.02
28.6%
13.01
 
14.3%
3.01
 
14.3%

Most occurring characters

ValueCountFrequency (%)
.7
31.8%
07
31.8%
14
18.2%
52
 
9.1%
32
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15
68.2%
Other Punctuation7
31.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07
46.7%
14
26.7%
52
 
13.3%
32
 
13.3%
Other Punctuation
ValueCountFrequency (%)
.7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common22
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.7
31.8%
07
31.8%
14
18.2%
52
 
9.1%
32
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII22
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.7
31.8%
07
31.8%
14
18.2%
52
 
9.1%
32
 
9.1%

2nd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)66.7%
Missing41
Missing (%)82.0%
Infinite0
Infinite (%)0.0%
Mean3.222222222
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:39.921117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile6.6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.223610677
Coefficient of variation (CV)0.6900860723
Kurtosis-0.853463668
Mean3.222222222
Median Absolute Deviation (MAD)2
Skewness0.6070845751
Sum29
Variance4.944444444
MonotonicityNot monotonic
2022-05-09T16:59:40.053821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
13
 
6.0%
42
 
4.0%
71
 
2.0%
61
 
2.0%
31
 
2.0%
21
 
2.0%
(Missing)41
82.0%
ValueCountFrequency (%)
13
6.0%
21
 
2.0%
31
 
2.0%
42
4.0%
61
 
2.0%
71
 
2.0%
ValueCountFrequency (%)
71
 
2.0%
61
 
2.0%
42
4.0%
31
 
2.0%
21
 
2.0%
13
6.0%

3rd
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)50.0%
Missing40
Missing (%)80.0%
Memory size528.0 B
2.0
1.0
5.0
4.0
6.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st row4.0
2nd row5.0
3rd row5.0
4th row6.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.03
 
6.0%
1.03
 
6.0%
5.02
 
4.0%
4.01
 
2.0%
6.01
 
2.0%
(Missing)40
80.0%

Length

2022-05-09T16:59:40.200822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T16:59:40.348791image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
2.03
30.0%
1.03
30.0%
5.02
20.0%
4.01
 
10.0%
6.01
 
10.0%

Most occurring characters

ValueCountFrequency (%)
.10
33.3%
010
33.3%
23
 
10.0%
13
 
10.0%
52
 
6.7%
41
 
3.3%
61
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20
66.7%
Other Punctuation10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
50.0%
23
 
15.0%
13
 
15.0%
52
 
10.0%
41
 
5.0%
61
 
5.0%
Other Punctuation
ValueCountFrequency (%)
.10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.10
33.3%
010
33.3%
23
 
10.0%
13
 
10.0%
52
 
6.7%
41
 
3.3%
61
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.10
33.3%
010
33.3%
23
 
10.0%
13
 
10.0%
52
 
6.7%
41
 
3.3%
61
 
3.3%

4th
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)45.5%
Missing39
Missing (%)78.0%
Memory size528.0 B
1.0
7.0
4.0
3.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)27.3%

Sample

1st row1.0
2nd row7.0
3rd row4.0
4th row7.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.06
 
12.0%
7.02
 
4.0%
4.01
 
2.0%
3.01
 
2.0%
2.01
 
2.0%
(Missing)39
78.0%

Length

2022-05-09T16:59:40.487929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T16:59:40.641958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.06
54.5%
7.02
 
18.2%
4.01
 
9.1%
3.01
 
9.1%
2.01
 
9.1%

Most occurring characters

ValueCountFrequency (%)
.11
33.3%
011
33.3%
16
18.2%
72
 
6.1%
41
 
3.0%
31
 
3.0%
21
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22
66.7%
Other Punctuation11
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011
50.0%
16
27.3%
72
 
9.1%
41
 
4.5%
31
 
4.5%
21
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common33
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.11
33.3%
011
33.3%
16
18.2%
72
 
6.1%
41
 
3.0%
31
 
3.0%
21
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.11
33.3%
011
33.3%
16
18.2%
72
 
6.1%
41
 
3.0%
31
 
3.0%
21
 
3.0%

5th
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)35.7%
Missing36
Missing (%)72.0%
Memory size528.0 B
1.0
2.0
3.0
5.0
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters42
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)14.3%

Sample

1st row1.0
2nd row3.0
3rd row2.0
4th row2.0
5th row5.0

Common Values

ValueCountFrequency (%)
1.06
 
12.0%
2.04
 
8.0%
3.02
 
4.0%
5.01
 
2.0%
4.01
 
2.0%
(Missing)36
72.0%

Length

2022-05-09T16:59:40.783960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T16:59:40.942959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.06
42.9%
2.04
28.6%
3.02
 
14.3%
5.01
 
7.1%
4.01
 
7.1%

Most occurring characters

ValueCountFrequency (%)
.14
33.3%
014
33.3%
16
14.3%
24
 
9.5%
32
 
4.8%
51
 
2.4%
41
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28
66.7%
Other Punctuation14
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014
50.0%
16
21.4%
24
 
14.3%
32
 
7.1%
51
 
3.6%
41
 
3.6%
Other Punctuation
ValueCountFrequency (%)
.14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common42
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.14
33.3%
014
33.3%
16
14.3%
24
 
9.5%
32
 
4.8%
51
 
2.4%
41
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII42
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.14
33.3%
014
33.3%
16
14.3%
24
 
9.5%
32
 
4.8%
51
 
2.4%
41
 
2.4%

6th
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)38.5%
Missing37
Missing (%)74.0%
Memory size528.0 B
1.0
3.0
2.0
4.0
6.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)15.4%

Sample

1st row2.0
2nd row1.0
3rd row4.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.06
 
12.0%
3.03
 
6.0%
2.02
 
4.0%
4.01
 
2.0%
6.01
 
2.0%
(Missing)37
74.0%

Length

2022-05-09T16:59:41.086771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T16:59:41.240799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.06
46.2%
3.03
23.1%
2.02
 
15.4%
4.01
 
7.7%
6.01
 
7.7%

Most occurring characters

ValueCountFrequency (%)
.13
33.3%
013
33.3%
16
15.4%
33
 
7.7%
22
 
5.1%
41
 
2.6%
61
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26
66.7%
Other Punctuation13
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013
50.0%
16
23.1%
33
 
11.5%
22
 
7.7%
41
 
3.8%
61
 
3.8%
Other Punctuation
ValueCountFrequency (%)
.13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.13
33.3%
013
33.3%
16
15.4%
33
 
7.7%
22
 
5.1%
41
 
2.6%
61
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.13
33.3%
013
33.3%
16
15.4%
33
 
7.7%
22
 
5.1%
41
 
2.6%
61
 
2.6%

7th
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)23.5%
Missing33
Missing (%)66.0%
Memory size528.0 B
1.0
2.0
3.0
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters51
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.9%

Sample

1st row1.0
2nd row3.0
3rd row2.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.09
 
18.0%
2.05
 
10.0%
3.02
 
4.0%
4.01
 
2.0%
(Missing)33
66.0%

Length

2022-05-09T16:59:41.384083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T16:59:41.540083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.09
52.9%
2.05
29.4%
3.02
 
11.8%
4.01
 
5.9%

Most occurring characters

ValueCountFrequency (%)
.17
33.3%
017
33.3%
19
17.6%
25
 
9.8%
32
 
3.9%
41
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number34
66.7%
Other Punctuation17
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
017
50.0%
19
26.5%
25
 
14.7%
32
 
5.9%
41
 
2.9%
Other Punctuation
ValueCountFrequency (%)
.17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common51
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.17
33.3%
017
33.3%
19
17.6%
25
 
9.8%
32
 
3.9%
41
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII51
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.17
33.3%
017
33.3%
19
17.6%
25
 
9.8%
32
 
3.9%
41
 
2.0%

T4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)64.3%
Missing36
Missing (%)72.0%
Infinite0
Infinite (%)0.0%
Mean8.285714286
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:41.668045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.25
median4
Q316.25
95-th percentile22.4
Maximum25
Range24
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.597213962
Coefficient of variation (CV)1.037594789
Kurtosis-0.7813728556
Mean8.285714286
Median Absolute Deviation (MAD)3
Skewness0.9095395819
Sum116
Variance73.91208791
MonotonicityNot monotonic
2022-05-09T16:59:41.809044image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
14
 
8.0%
182
 
4.0%
32
 
4.0%
251
 
2.0%
211
 
2.0%
61
 
2.0%
111
 
2.0%
51
 
2.0%
21
 
2.0%
(Missing)36
72.0%
ValueCountFrequency (%)
14
8.0%
21
 
2.0%
32
4.0%
51
 
2.0%
61
 
2.0%
111
 
2.0%
182
4.0%
211
 
2.0%
251
 
2.0%
ValueCountFrequency (%)
251
 
2.0%
211
 
2.0%
182
4.0%
111
 
2.0%
61
 
2.0%
51
 
2.0%
32
4.0%
21
 
2.0%
14
8.0%

T7
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15
Distinct (%)55.6%
Missing23
Missing (%)46.0%
Infinite0
Infinite (%)0.0%
Mean7.518518519
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:41.944783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q310.5
95-th percentile25.7
Maximum29
Range28
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation8.846426355
Coefficient of variation (CV)1.176618284
Kurtosis0.7461617525
Mean7.518518519
Median Absolute Deviation (MAD)2
Skewness1.39325077
Sum203
Variance78.25925926
MonotonicityNot monotonic
2022-05-09T16:59:42.081785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
19
 
18.0%
24
 
8.0%
32
 
4.0%
291
 
2.0%
251
 
2.0%
241
 
2.0%
261
 
2.0%
151
 
2.0%
121
 
2.0%
111
 
2.0%
Other values (5)5
 
10.0%
(Missing)23
46.0%
ValueCountFrequency (%)
19
18.0%
24
8.0%
32
 
4.0%
41
 
2.0%
71
 
2.0%
81
 
2.0%
91
 
2.0%
101
 
2.0%
111
 
2.0%
121
 
2.0%
ValueCountFrequency (%)
291
2.0%
261
2.0%
251
2.0%
241
2.0%
151
2.0%
121
2.0%
111
2.0%
101
2.0%
91
2.0%
81
2.0%

Debut
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)40.8%
Missing1
Missing (%)2.0%
Memory size528.0 B
1992–93
22 
1993–94
1999–2000
 
2
1996–97
 
2
2002–03
 
2
Other values (15)
18 

Length

Max length9
Median length7
Mean length7.081632653
Min length7

Characters and Unicode

Total characters347
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)24.5%

Sample

1st row1992–93
2nd row1992–93
3rd row1992–93
4th row1992–93
5th row1992–93

Common Values

ValueCountFrequency (%)
1992–9322
44.0%
1993–943
 
6.0%
1999–20002
 
4.0%
1996–972
 
4.0%
2002–032
 
4.0%
2008–092
 
4.0%
2017–182
 
4.0%
2003–042
 
4.0%
1997–981
 
2.0%
2010–111
 
2.0%
Other values (10)10
20.0%

Length

2022-05-09T16:59:42.231307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1992–9322
44.9%
1993–943
 
6.1%
1999–20002
 
4.1%
1996–972
 
4.1%
2002–032
 
4.1%
2008–092
 
4.1%
2017–182
 
4.1%
2003–042
 
4.1%
2009–101
 
2.0%
2001–021
 
2.0%
Other values (10)10
20.4%

Most occurring characters

ValueCountFrequency (%)
9103
29.7%
149
14.1%
49
14.1%
244
12.7%
043
12.4%
330
 
8.6%
47
 
2.0%
66
 
1.7%
76
 
1.7%
86
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number298
85.9%
Dash Punctuation49
 
14.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9103
34.6%
149
16.4%
244
14.8%
043
14.4%
330
 
10.1%
47
 
2.3%
66
 
2.0%
76
 
2.0%
86
 
2.0%
54
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9103
29.7%
149
14.1%
49
14.1%
244
12.7%
043
12.4%
330
 
8.6%
47
 
2.0%
66
 
1.7%
76
 
1.7%
86
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII298
85.9%
Punctuation49
 
14.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9103
34.6%
149
16.4%
244
14.8%
043
14.4%
330
 
10.1%
47
 
2.3%
66
 
2.0%
76
 
2.0%
86
 
2.0%
54
 
1.3%
Punctuation
ValueCountFrequency (%)
49
100.0%

Since/Last App.
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)55.1%
Missing1
Missing (%)2.0%
Memory size528.0 B
2012–13
2017–18
2016–17
2020–21
2018–19
 
3
Other values (22)
30 

Length

Max length10
Median length7
Mean length7.448979592
Min length7

Characters and Unicode

Total characters365
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)28.6%

Sample

1st row1992–93[a]
2nd row1992–93[b]
3rd row1992–93[c]
4th row1992–93[d]
5th row1992–93[e]

Common Values

ValueCountFrequency (%)
2012–134
 
8.0%
2017–184
 
8.0%
2016–174
 
8.0%
2020–214
 
8.0%
2018–193
 
6.0%
2019–202
 
4.0%
2010–112
 
4.0%
2011–122
 
4.0%
1993–942
 
4.0%
2014–152
 
4.0%
Other values (17)20
40.0%

Length

2022-05-09T16:59:42.390307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012–134
 
8.2%
2016–174
 
8.2%
2020–214
 
8.2%
2017–184
 
8.2%
2018–193
 
6.1%
2000–012
 
4.1%
2021–222
 
4.1%
1999–20002
 
4.1%
2014–152
 
4.1%
1993–942
 
4.1%
Other values (17)20
40.8%

Most occurring characters

ValueCountFrequency (%)
172
19.7%
269
18.9%
067
18.4%
49
13.4%
943
11.8%
314
 
3.8%
711
 
3.0%
810
 
2.7%
[6
 
1.6%
]6
 
1.6%
Other values (9)18
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number298
81.6%
Dash Punctuation49
 
13.4%
Open Punctuation6
 
1.6%
Close Punctuation6
 
1.6%
Lowercase Letter6
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
172
24.2%
269
23.2%
067
22.5%
943
14.4%
314
 
4.7%
711
 
3.7%
810
 
3.4%
45
 
1.7%
65
 
1.7%
52
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
a1
16.7%
b1
16.7%
f1
16.7%
e1
16.7%
d1
16.7%
c1
16.7%
Dash Punctuation
ValueCountFrequency (%)
49
100.0%
Open Punctuation
ValueCountFrequency (%)
[6
100.0%
Close Punctuation
ValueCountFrequency (%)
]6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common359
98.4%
Latin6
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
172
20.1%
269
19.2%
067
18.7%
49
13.6%
943
12.0%
314
 
3.9%
711
 
3.1%
810
 
2.8%
[6
 
1.7%
]6
 
1.7%
Other values (3)12
 
3.3%
Latin
ValueCountFrequency (%)
a1
16.7%
b1
16.7%
f1
16.7%
e1
16.7%
d1
16.7%
c1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII316
86.6%
Punctuation49
 
13.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
172
22.8%
269
21.8%
067
21.2%
943
13.6%
314
 
4.4%
711
 
3.5%
810
 
3.2%
[6
 
1.9%
]6
 
1.9%
45
 
1.6%
Other values (8)13
 
4.1%
Punctuation
ValueCountFrequency (%)
49
100.0%

Relegated
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)11.9%
Missing8
Missing (%)16.0%
Memory size528.0 B
1.0
17 
2.0
11 
3.0
4.0
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters126
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.017
34.0%
2.011
22.0%
3.09
18.0%
4.03
 
6.0%
5.02
 
4.0%
(Missing)8
16.0%

Length

2022-05-09T16:59:42.541307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T16:59:42.700312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.017
40.5%
2.011
26.2%
3.09
21.4%
4.03
 
7.1%
5.02
 
4.8%

Most occurring characters

ValueCountFrequency (%)
.42
33.3%
042
33.3%
117
13.5%
211
 
8.7%
39
 
7.1%
43
 
2.4%
52
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number84
66.7%
Other Punctuation42
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
042
50.0%
117
20.2%
211
 
13.1%
39
 
10.7%
43
 
3.6%
52
 
2.4%
Other Punctuation
ValueCountFrequency (%)
.42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common126
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.42
33.3%
042
33.3%
117
13.5%
211
 
8.7%
39
 
7.1%
43
 
2.4%
52
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.42
33.3%
042
33.3%
117
13.5%
211
 
8.7%
39
 
7.1%
43
 
2.4%
52
 
1.6%

Best
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17
Distinct (%)34.7%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean7.897959184
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:42.841527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile19
Maximum22
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.65407264
Coefficient of variation (CV)0.7158903342
Kurtosis-0.06724851007
Mean7.897959184
Median Absolute Deviation (MAD)3
Skewness0.8143237612
Sum387
Variance31.96853741
MonotonicityNot monotonic
2022-05-09T16:59:43.099608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
17
14.0%
77
14.0%
85
10.0%
94
8.0%
63
 
6.0%
33
 
6.0%
53
 
6.0%
23
 
6.0%
193
 
6.0%
102
 
4.0%
Other values (7)9
18.0%
ValueCountFrequency (%)
17
14.0%
23
6.0%
33
6.0%
41
 
2.0%
53
6.0%
63
6.0%
77
14.0%
85
10.0%
94
8.0%
102
 
4.0%
ValueCountFrequency (%)
221
 
2.0%
193
6.0%
181
 
2.0%
171
 
2.0%
162
 
4.0%
151
 
2.0%
112
 
4.0%
102
 
4.0%
94
8.0%
85
10.0%

WP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct46
Distinct (%)93.9%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean0.3120153386
Minimum0.119047619
Maximum0.6166965889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:43.243643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.119047619
5-th percentile0.1968421053
Q10.2631578947
median0.2887931034
Q30.3329853862
95-th percentile0.5301615799
Maximum0.6166965889
Range0.4976489698
Interquartile range (IQR)0.06982749148

Descriptive statistics

Standard deviation0.0956468349
Coefficient of variation (CV)0.3065452978
Kurtosis2.219963284
Mean0.3120153386
Median Absolute Deviation (MAD)0.02688834154
Skewness1.269975852
Sum15.28875159
Variance0.009148317027
MonotonicityNot monotonic
2022-05-09T16:59:43.407636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.53590664272
 
4.0%
0.26315789472
 
4.0%
0.23684210532
 
4.0%
0.3030303031
 
2.0%
0.30827067671
 
2.0%
0.29136690651
 
2.0%
0.27443609021
 
2.0%
0.28195488721
 
2.0%
0.29699248121
 
2.0%
0.28571428571
 
2.0%
Other values (36)36
72.0%
ValueCountFrequency (%)
0.1190476191
2.0%
0.15789473681
2.0%
0.18421052631
2.0%
0.21578947371
2.0%
0.22368421051
2.0%
0.23684210532
4.0%
0.25164473681
2.0%
0.25187969921
2.0%
0.25563909771
2.0%
0.26190476191
2.0%
ValueCountFrequency (%)
0.61669658891
2.0%
0.53590664272
4.0%
0.52154398561
2.0%
0.48051948051
2.0%
0.43087971271
2.0%
0.40909090911
2.0%
0.37643678161
2.0%
0.37048192771
2.0%
0.36535008981
2.0%
0.3411
2.0%

predWP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct49
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean0.3965227245
Minimum0.1222135315
Maximum0.8164450691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:43.569921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.1222135315
5-th percentile0.1777386934
Q10.3142780426
median0.3657141125
Q30.4490885661
95-th percentile0.7495255475
Maximum0.8164450691
Range0.6942315376
Interquartile range (IQR)0.1348105236

Descriptive statistics

Standard deviation0.1545374718
Coefficient of variation (CV)0.3897316906
Kurtosis1.165683778
Mean0.3965227245
Median Absolute Deviation (MAD)0.05692973098
Skewness1.044728092
Sum19.4296135
Variance0.02388183018
MonotonicityNot monotonic
2022-05-09T16:59:43.733078image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.81644506911
 
2.0%
0.34782999261
 
2.0%
0.38962238641
 
2.0%
0.38220112941
 
2.0%
0.36510735861
 
2.0%
0.32388383321
 
2.0%
0.37125539921
 
2.0%
0.30451024271
 
2.0%
0.29395095281
 
2.0%
0.28144847541
 
2.0%
Other values (39)39
78.0%
ValueCountFrequency (%)
0.12221353151
2.0%
0.16915853211
2.0%
0.17560975611
2.0%
0.18093209931
2.0%
0.1953692751
2.0%
0.23897439641
2.0%
0.28144847541
2.0%
0.29395095281
2.0%
0.3006081041
2.0%
0.30382532561
2.0%
ValueCountFrequency (%)
0.81644506911
2.0%
0.75972689091
2.0%
0.75110711111
2.0%
0.74715320211
2.0%
0.69121491821
2.0%
0.58970227021
2.0%
0.55695687551
2.0%
0.51152483981
2.0%
0.51090382871
2.0%
0.49181602441
2.0%

factor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct49
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean1.247598043
Minimum0.6428024218
Maximum1.519829634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-05-09T16:59:43.900326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.6428024218
5-th percentile0.8952777714
Q11.180137475
median1.271250098
Q31.365995341
95-th percentile1.42660681
Maximum1.519829634
Range0.877027212
Interquartile range (IQR)0.1858578658

Descriptive statistics

Standard deviation0.1705735311
Coefficient of variation (CV)0.1367215443
Kurtosis3.685665777
Mean1.247598043
Median Absolute Deviation (MAD)0.09474524258
Skewness-1.681416962
Sum61.13230409
Variance0.0290953295
MonotonicityNot monotonic
2022-05-09T16:59:44.061325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1.3239007381
 
2.0%
1.1801374751
 
2.0%
1.2638970091
 
2.0%
1.3117520241
 
2.0%
1.3303911971
 
2.0%
1.1487079951
 
2.0%
1.2500498251
 
2.0%
1.065785851
 
2.0%
1.1498669621
 
2.0%
1.1173924541
 
2.0%
Other values (39)39
78.0%
ValueCountFrequency (%)
0.64280242181
2.0%
0.77401903271
2.0%
0.78507890961
2.0%
1.0605760641
2.0%
1.065785851
2.0%
1.0751160431
2.0%
1.1074423251
2.0%
1.1173924541
2.0%
1.1487079951
2.0%
1.1498669621
2.0%
ValueCountFrequency (%)
1.5198296341
2.0%
1.4384742891
2.0%
1.4325794621
2.0%
1.4176478331
2.0%
1.4050691771
2.0%
1.4015633531
2.0%
1.4000950161
2.0%
1.3907467541
2.0%
1.3760933871
2.0%
1.3736348271
2.0%

Interactions

2022-05-09T16:59:33.197550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:05.245802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:07.191271image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:09.246740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:11.140952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:13.215896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:15.143070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:17.233019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:19.158625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:21.257810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:23.178582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:25.252139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:27.190331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2022-05-09T16:59:13.087720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:15.017230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:17.105512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:19.032655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:21.125781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:23.050583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:25.122139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:27.055035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:29.084594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:30.994782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-05-09T16:59:33.069517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2022-05-09T16:59:44.228324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T16:59:44.463750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T16:59:44.701728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T16:59:44.923727image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T16:59:45.122438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T16:59:35.229629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T16:59:35.809069image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T16:59:36.129373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T16:59:36.492887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Pos.ClubSeasonsPldWDLGFGAGDPts1st2nd3rd4th5th6th7thT4T7DebutSince/Last App.RelegatedBestWPpredWPfactor
01Manchester United291114687247180212810091119230813.07.04.01.01.02.01.025.029.01992–931992–93[a]NaN1.00.6166970.8164451.323901
12Arsenal2911145972812361956110085620723.06.05.07.03.01.0NaN21.025.01992–931992–93[b]NaN1.00.5359070.7597271.417648
23Chelsea2911145972732441897109280520645.04.05.04.02.04.0NaN18.024.01992–931992–93[c]NaN1.00.5359070.7511071.401563
34Liverpool2911145812742591927112180620171.04.06.07.02.03.03.018.026.01992–931992–93[d]NaN1.00.5215440.7471531.432579
45Tottenham Hotspur291114480276358167613982781716NaN1.02.03.05.02.02.06.015.01992–931992–93[e]NaN2.00.4308800.5897021.368601
56Manchester City249244442042761559104251715365.03.02.01.01.0NaNNaN11.012.01992–932002–032.01.00.4805190.6912151.438474
67Everton29111440731439314481415331535NaNNaNNaN1.03.03.04.01.011.01992–931992–93[f]NaN4.00.3653500.5115251.400095
78Newcastle United2699636925437313331355−221361NaN2.02.01.02.01.01.05.09.01993–942017–182.02.00.3704820.4918161.327503
89Aston Villa26100034129036912131299−861313NaN1.0NaN1.01.06.01.02.010.01992–932019–201.02.00.3410000.4658041.365995
910West Ham United2595831924539411751378−2031202NaNNaNNaNNaN1.01.02.0NaN4.01993–942012–132.05.00.3329850.4209851.264276

Last rows

Pos.ClubSeasonsPldWDLGFGAGDPts1st2nd3rd4th5th6th7thT4T7DebutSince/Last App.RelegatedBestWPpredWPfactor
4041Brighton & Hove Albion4152365066148214−66158NaNNaNNaNNaNNaNNaNNaNNaNNaN2017–182017–18NaN15.00.2368420.3235451.366079
4142Reading3114322359136186−50119NaNNaNNaNNaNNaNNaNNaNNaNNaN2006–072012–132.08.00.2807020.3483761.241091
4243Oldham Athletic284222339105142−3789NaNNaNNaNNaNNaNNaNNaNNaNNaN1992–931993–941.019.00.2619050.3534901.349689
4344Cardiff City27617134666143−7764NaNNaNNaNNaNNaNNaNNaNNaNNaN2013–142018–192.018.00.2236840.1756100.785079
4445Bradford City27614204268138−7062NaNNaNNaNNaNNaNNaNNaNNaNNaN1999–20002000–011.017.00.1842110.1953691.060576
4546Huddersfield Town27612174750134−8453NaNNaNNaNNaNNaNNaNNaNNaNNaN2017–182018–191.016.00.1578950.1222140.774019
4647Blackpool138109195578−2339NaNNaNNaNNaNNaNNaNNaNNaNNaN2010–112010–111.019.00.2631580.3320891.261939
4748Barnsley138105233782−4535NaNNaNNaNNaNNaNNaNNaNNaNNaN1997–981997–981.019.00.2631580.1691590.642802
4849Swindon Town1425152247100−5330NaNNaNNaNNaNNaNNaNNaNNaNNaN1993–941993–941.022.00.1190480.1809321.519830
4950Brentford000000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN